Which Districts Run India’s Toughest Schools Most Efficiently? New Study Ranks Ladakh and Jammu & Kashmir

When India reorganized its map in 2019, dissolving the old state of Jammu & Kashmir and carving out two new Union Territories, it inherited a question that economists had never systematically answered: how efficiently do the schools of this mountainous, conflict-scarred region actually convert classrooms, teachers, and toilets into educated children? A new study by Anwar Hussain and Halima Sadia Rizvi of Jamia Millia Islamia in New Delhi, published in Discover Education, delivers the first district-level efficiency audit of primary and secondary schooling across all 22 districts of Ladakh and Jammu & Kashmir, and its verdict is strikingly uneven.

The researchers drew on the Unified District Information System for Education Plus, the Indian government’s exhaustive annual school census, for the academic years 2020–21 and 2021–22. From these records they assembled a portrait of each district’s educational machinery: ten infrastructure variables, including separate rooms for headmasters, functional electricity, playgrounds, libraries, furniture, toilets, drinking water, and computer facilities, were compressed into a single composite index using principal component analysis. Against this resource base they measured outputs such as promotion rates, girls’ enrollment shares, the number of schools, and the percentage of trained teachers.

To judge which districts squeeze the most schooling from their inputs, the team deployed Data Envelopment Analysis, a non-parametric linear programming technique pioneered by Charnes, Cooper, and Rhodes in 1978. DEA works by constructing an efficiency frontier from the best-performing districts and then scoring every other district by its distance from that frontier, with a score of 1 denoting full technical efficiency. The analysis was run under both constant and variable returns to scale assumptions, using an input-oriented model that asks how much a district could proportionally trim its resources while holding outcomes fixed, a reasonable framing since administrators control budgets more tightly than results.

The headline finding is that Kargil and Pulwama sit on the frontier across both years and all three education levels, primary, upper primary, and secondary, in both CRS and VRS models. These two districts, one in Ladakh’s high-altitude desert and one in the Kashmir Valley, achieved steady improvements in outcomes as teacher training expanded while other inputs changed little. Ganderbal and Rajouri proved efficient in primary and upper primary education, while Kathua, Leh, and Srinagar demonstrated efficiency in primary and secondary schooling. Mean VRS efficiency scores were remarkably high throughout: 0.980 for primary schools in 2020–21, 0.952 for upper primary, and 0.943 for secondary, with only marginal drift by the following year.

Beneath the averages, the returns-to-scale diagnostics reveal where resources are being misallocated. In 2020–21, primary schools in Baramulla and Jammu exhibited decreasing returns to scale, meaning a 1 percent increase in all inputs yielded less than 1 percent more output, a signature of input congestion. Both recovered the next year as infrastructure and trained-teacher percentages improved. By 2021–22, every district’s primary schools showed either increasing or constant returns to scale. Doda and Reasi, though below average efficiency, displayed increasing returns to scale, suggesting these underperforming districts would benefit more from additional resources than from austerity, since their schools have absorptive capacity waiting to be filled.

Efficiency alone, however, is a static snapshot. To capture whether districts are getting better or worse over time, the study applied the Malmquist Productivity Index, which measures total factor productivity change between two periods as the geometric mean of distance functions relative to shifting frontiers. The index decomposes into technical efficiency change, itself split into pure and scale components, and technological change, which tracks movement of the frontier itself. An index above 1 signals improvement; below 1, decline.

For primary schools, the verdict was sobering: the average Malmquist index was 0.905, a 9.5 percent productivity decline, driven almost entirely by technological change of 0.906. Nine districts recorded productivity losses, with the steepest in Samba at 78.2 percent, Rajouri at 51.7 percent, Kishtwar at 47.7 percent, and Kathua at 34 percent. Samba’s technological component alone fell by roughly 77.2 percent alongside declines in technical and pure efficiency. Yet thirteen districts, including Anantnag, Badgam, Bandipora, Baramulla, Doda, Ganderbal, Kargil, Kulgam, Kupwara, Pulwama, Ramban, Reasi, and Shopian, posted productivity gains, some attributable purely to technological advancement and others to improvements in management and scale efficiency.

The most consequential pattern emerges at the top of the system. In upper primary schools, average technical, pure, scale, and total factor productivity indexes all equaled 1, with only technological change declining to 0.894; Kargil’s productivity surged 263.6 percent through technological change. But in secondary schools, the picture darkens: productivity ranged from 0.463 to 1.000, fifteen districts recorded declining total factor productivity, and not a single secondary school district demonstrated an increase. Seven districts, including Bandipora, Doda, Ganderbal, Kupwara, Punch, Reasi, and Shopian, held perfectly steady. The study’s authors note that while primary and upper primary units show productivity growth from technical, technological, pure, and scale factors, secondary schooling’s changes flow solely through technological channels, with no efficiency gains to offset frontier slippage.

The two Ladakh districts, Leh and Kargil, performed well across all three levels, which the researchers attribute to central government funding of their schools. The policy prescriptions are region-specific: Ladakh needs primary schools upgraded to upper primary or secondary status to improve retention, plus infrastructure hardening for harsh weather, transportation, and in-service teacher training. In Jammu and Kashmir, trained teachers must be deployed to rural areas, with new schools in Kupwara, Baramulla, Rajouri, and Poonch, supported by residential facilities, hardship allowances, and safety measures.

The study’s limitations are candid: a two-year window, a restricted set of input and output variables, no district-level learning outcomes, and no second-stage regression analysis. Still, the core message resonates far beyond the Himalayas. Increased educational expenditure and more teachers do not reliably translate into better schooling; several underperforming districts show high student-teacher ratios and underutilized infrastructure, hallmarks of resource misallocation rather than input shortage. Optimizing existing resources, the authors conclude, is more likely to lift productivity than indiscriminate expansion, a finding with direct implications for achieving Sustainable Development Goal 4 in India’s newest Union Territories and beyond.

,

Subject of Research: A district-level analysis of school efficiency and productivity in Ladakh and Jammu & Kashmir finds Kargil and Pulwama consistently on top, while secondary schools show no productivity gains.

Article Title: Evaluating the efficiency and productivity change of primary and secondary school education in Ladakh and Jammu & Kashmir

Article References: Hussain, A., & Rizvi, H. S. (2026). Evaluating the efficiency and productivity change of primary and secondary school education in Ladakh and Jammu & Kashmir. Discover Education, 5(1), Article 834. https://doi.org/10.1007/s44217-026-02100-w

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02100-w

Keywords: school efficiency, Data Envelopment Analysis, Malmquist index, Ladakh, Jammu and Kashmir, UDISE+, education productivity, SDG 4, teacher training, Kargil, Evaluating, efficiency